Notes

Large creation and also characterization associated with SARS-CoV-2 total antigen regarding serological test development.
Respite care can provide a chance for family caregivers to take a temporary and flexible break from their long-term caregiving work. Despite its beneficial aspects and value, there is little research on how technology might mitigate barriers to using respite care. The purpose of this paper is to understand the current practices and challenges that people face within the ecosystem of respite care work in the context of in-home care. Based on an in-depth interview study of 18 primary family caregivers, respite family caregivers, and respite professional caregivers, we identified different relationships, phases, and needs of each stakeholder and issues of trust and information sharing that need improvement. We discuss design considerations on how future information and communication technologies (ICTs) could mitigate the barriers identified in this work.Many stakeholders can be involved in supporting a child's development, including parents, pediatricians, and educators. These stakeholders struggle to collaborate, and experts suggest that health information technology could improve their communication. Trust, based on perceptions of competence, benevolence, and integrity is fundamental to supporting information sharing, so information technologies should address trust between stakeholders. We engaged 75 parents and 60 healthcare workers with two surveys to explore this topic. We first elicited the types of information parents and healthcare workers use to form perceptions of competence, benevolence, and integrity. We then designed and tested user profile prototypes listing the elicited information to see if it builds trust in previously unknown professionals. We discovered that providing information related to personal characteristics, relationships, professional experience, and workplace practices can support trust and the sharing of information. This work has implications for designing informative electronic user interfaces to support interprofessional trust.Patient-centered care is an essential component of quality health care. To support patient-centered care initiatives at our institution, we created a feature in our EHR to centrally view information about the patient's values, goals and preferences. We applied user-centered design methods to ensure that the aggregate view was easy to use and would meet user needs. We created a six-week plan to iterate through increasingly detailed design mock-ups. We defined 7 user stories that later served as a basis for user testing scripts. We conducted user testing on our third design iteration; we reached theme saturation with 8 testing sessions. mTOR activation We incorporated findings into the fourth design (week 6) but continued to refine the design in parallel to development (through week 20+). The advance directives section required the most attention. We will use a pilot and additional user testing to validate the design and to inform future versions.Research has shown that health outcomes are significantly driven by patient's social and economic needs and environment, commonly referred to as the social determinants of health (SDoH). Standardized documentation of social and economic needs in healthcare are underutilized. This study examines the prevalence of documented social and economic needs (Z-codes) in a nationwide inpatient database and the association with emergency department (ED) admissions. Multivariate logistic regression was used to assess the effect of social and economic Z-codes on hospital admission through the ED. Payer source, gender, age at admission, comorbidity count, and median ZIP code income quartile covariates were included in the logistic regression analyses. Patients with documented social and economic Z-codes were significantly more likely to be admitted through the ED than those without documented social and economic needs, after adjusting for covariates. Standardized and widespread collection of these valuable Z-codes within EHR systems or administrative claims databases can help with targeted resource allocation to alleviate possible barriers to care and mitigate ED utilization.It is difficult to arrive at an efficient and widely acceptable set of common data elements (CDEs). Trial outcomes, as defined in a clinical trial registry, offer a large set of elements to analyze. However, all clinical trial outcomes is an overwhelming amount of information. One way to reduce this amount of data to a usable volume is to only use a subset of trials. Our method uses a subset of trials by considering trials that support drug approval (pivotal trials) by Food and Drug Administration. We identified a set of pivotal trials from FDA drug approval documents and used primary outcomes data for these trials to identify a set of important CDEs. We identified 76 CDEs out of a set of 172 data elements from 192 pivotal trials for 100 drugs. This set of CDEs, grouped by medical condition, can be considered as containing the most significant data elements.Wrist accelerometers for assessing hallmark measures of physical activity (PA) are rapidly growing with the advent of smartwatch technology. Given the growing popularity of wrist-worn accelerometers, there needs to be a rigorous evaluation for recognizing (PA) type and estimating energy expenditure (EE) across the lifespan. Participants (66% women, aged 20-89 yrs) performed a battery of 33 daily activities in a standardized laboratory setting while a tri-axial accelerometer collected data from the right wrist. A portable metabolic unit was worn to measure metabolic intensity. We built deep learning networks to extract spatial and temporal representations from the time-series data, and used them to recognize PA type and estimate EE. The deep learning models resulted in high performance; the F1 score was 0.82, 0.81, and 95 for recognizing sedentary, locomotor, and lifestyle activities, respectively. The root mean square error was 1.1 (+/-0.13) for the estimation of EE.Applying state-of-the-art machine learning and natural language processing on approximately one million of teleconsultation records, we developed a triage system, now certified and in use at the largest European telemedicine provider. The system evaluates care alternatives through interactions with patients via a mobile application. Reasoning on an initial set of provided symptoms, the triage application generates AI-powered, personalized questions to better characterize the problem and recommends the most appropriate point of care and time frame for a consultation. The underlying technology was developed to meet the needs for performance, transparency, user acceptance and ease of use, central aspects to the adoption of AI-based decision support systems. Providing such remote guidance at the beginning of the chain of care has significant potential for improving cost efficiency, patient experience and outcomes. Being remote, always available and highly scalable, this service is fundamental in high demand situations, such as the current COVID-19 outbreak.
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